Doing feature generation in serving_input_fn for Tensorflow model - python-3.x

Question

I've been playing around with BERT and TensorFlow following the example here and have a trained working model.
I then wanted to save and deploy the model, so used the export_saved_model function, which requires you build a serving_input_fn to handle any incoming requests when the model is reloaded.
I wanted to be able to pass a single string for sentiment analysis to the deployed model, rather than having a theoretical client side application do the tokenisation and feature generation etc, so tried to write an input function that would handle that and pass the constructed features to the model. Is this possible? I wrote the following which I feel should do what I want:
import json
import base64
def plain_text_serving_input_fn():
input_string = tf.placeholder(dtype=tf.string, shape=None, name='input_string_text')
# What format to expect input in.
receiver_tensors = {'input_text': input_string}
input_examples = [run_classifier.InputExample(guid="", text_a = str(input_string), text_b = None, label = 0)] # here, "" is just a dummy label
input_features = run_classifier.convert_examples_to_features(input_examples, label_list, MAX_SEQ_LENGTH, tokenizer)
variables = {}
for i in input_features:
variables["input_ids"] = i.input_ids
variables["input_mask"] = i.input_mask
variables["segment_ids"] = i.segment_ids
variables["label_id"] = i.label_id
feature_spec = {
"input_ids" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
"input_mask" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
"segment_ids" : tf.FixedLenFeature([MAX_SEQ_LENGTH], tf.int64),
"label_ids" : tf.FixedLenFeature([], tf.int64)
}
string_variables = json.dumps(variables)
encode_input = base64.b64encode(string_variables.encode('utf-8'))
encode_string = base64.decodestring(encode_input)
features_to_input = tf.parse_example([encode_string], feature_spec)
return tf.estimator.export.ServingInputReceiver(features_to_input, receiver_tensors)
I would expect that this would allow me to call predict on my deployed model with
variables = {"input_text" : "This is some test input"}
predictor.predict(variables)
I've tried a range of variations of this (putting it in an array, converting to base 64 etc), but I get a range of errors either telling me
"error": "Failed to process element: 0 of 'instances' list. Error: Invalid argument: JSON Value: {\n \"input_text\": \"This is some test input\"\n} not formatted correctly for base64 data" }"
or
Object of type 'bytes' is not JSON serializable
I suspect I'm formatting my requests incorrectly, but I also can't find any examples of something similar being done in a serving_input_fn, so has anyone ever done something similar?